Method for personalized breaking news feed

ABSTRACT

In an approach to personalizing a news feed, a computing device monitors a user accessing content. The computing device determines a personal knowledge graph for the user based on the accessed content. Responsive to receiving a new piece of content that the user has not accessed, the computing device determines a novelty score for the new piece of content based on the personal knowledge graph. The computing device filters the new piece of content based on the novelty score.

TECHNICAL FIELD OF THE INVENTION

The present disclosure relates generally to the field of contentfiltering, and more particularly to deduplication and prioritization ofcontent in a news feed.

BACKGROUND OF THE INVENTION

Given a developing storyline or event in the news, a reader may preferto receive only the most relevant, up-to-date information from his orher content providers and avoid receiving redundant or duplicateinformation. Deduplication methods have developed as a means forcurating personalized content streams, so that redundant facts andnotifications are suppressed based on, for example, matching ofcharacter strings in incoming and previously presented content or adetermination that a notification concerning a particular article hasalready been sent.

SUMMARY

According to an embodiment of the present invention, acomputer-implemented method for personalized content filtering, themethod comprising: determining, by the one or more computer processors,a first story arc based on a first piece of content; creating, by theone or more computer processors, a personal knowledge graph, of only theuser, representing the user's individual knowledge related to the firststory arc; determining, by the one or more computer processors,responsive to receiving a second piece of content, wherein the secondpiece of content has not been accessed by the user, a second story arcbased on the second piece of content; determining, by the one or morecomputer processors, that the user has accessed the second piece ofcontent; updating, by the one or more computer processors, the personalknowledge graph based on the second piece of content, wherein the firstpiece of content and the second piece of content share a third storyarc, or determining a second personal knowledge graph based on thesecond story arc; determining, by the one or more computer processors,which information in the second piece of content the user has consumed;determining, by the one or more computer processors, a novelty score forthe second piece of content based on the personal knowledge graph; andfiltering, by the one or more computer processors, the second piece ofcontent based on the novelty score.

According to an embodiment of the present invention, a computer programproduct for personalized content filtering, the computer program productcomprising: one or more non-transitory computer readable storage mediaand program instructions stored on the one or more non-transitorycomputer readable storage media, the program instructions comprising:program instructions to determine a first story arc based on a firstpiece of content; program instructions to create a personal knowledgegraph, of only the user, representing the user's individual knowledgerelated to the first story arc; program instructions to determine,responsive to receiving a second piece of content, wherein the secondpiece of content has not been accessed by the user, a second story arcbased on the second piece of content; program instructions to determinethat the user has accessed the second piece of content; programinstructions to update the personal knowledge graph based on the secondpiece of content, wherein the first piece of content and the secondpiece of content share a third story arc, or determining a secondpersonal knowledge graph based on the second story arc; programinstructions to determine which information in the second piece ofcontent the user has consumed; program instructions to determine anovelty score for the second piece of content based on the personalknowledge graph; program instructions to filter the second piece ofcontent based on the personal knowledge graph; and program instructionsto filter the second piece of content based on the novelty score.

According to an embodiment of the present invention, a computer systemfor personalized content filtering, the computer system comprising: oneor more processors; one or more computer readable storage media; andprogram instructions stored on the one or more computer readable storagemedia for execution by at least one of the one or more processors, theprogram instructions comprising: program instructions to determine afirst story arc based on a first piece of content; program instructionsto create a personal knowledge graph, of only the user, representing theuser's individual knowledge related to the first story arc; programinstructions to determine, responsive to receiving a second piece ofcontent, wherein the second piece of content has not been accessed bythe user, a second story arc based on the second piece of content;program instructions to determine that the user has accessed the secondpiece of content; program instructions to update the personal knowledgegraph based on the second piece of content, wherein the first piece ofcontent and the second piece of content share a third story arc, ordetermining a second personal knowledge graph based on the second storyarc; program instructions to determine which information in the secondpiece of content the user has consumed; program instructions todetermine a novelty score for the second piece of content based on thepersonal knowledge graph; program instructions to filter the secondpiece of content based on the personal knowledge graph; and programinstructions to filter the second piece of content based on the noveltyscore.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram of an exemplary computingenvironment, in accordance with an embodiment of the present invention;

FIG. 2 is a flowchart depicting steps of a personalized contentfiltering method, in accordance with an embodiment of the presentinvention;

FIG. 3A-C shows exemplary knowledge graphs, in accordance with anembodiment of the present invention; and

FIG. 4 is a block diagram of components of the computing device in FIG.1 executing a personalized content filtering program, in accordance withan embodiment of the present invention.

DETAILED DESCRIPTION

Existing deduplication methods can summarize or prioritize a news feedbased on content-focused criteria, such as matched character strings innew and previously presented content. The present disclosure provides amethod for deduplicating and prioritizing content based onuser-knowledge criteria. Embodiments disclosed herein build auser-specific knowledge store that can be used to gauge the importanceand novelty of incoming information and content sources to a particularuser based on what the user has likely learned before. As used herein,the term “user” is not limited to contemplating a human user consumingcontent. The term “user” can also include, for example but withoutlimitation, an automated agent that receives an influx of contentelements and determines whether or not to read those content elements.

FIG. 1 shows a block diagram of a computing environment 100, inaccordance with an embodiment of the present invention. FIG. 1 isprovided for the purposes of illustration and does not imply anylimitations with regard to the environments in which differentembodiments can be implemented. Many modifications to the depictedenvironment can be made by those skilled in the art without departingfrom the scope of the invention as recited in the claims.

Computing environment 100 includes computing device 104, which can beinterconnected with other devices (not shown) over network 102. Network102 can be, for example, a telecommunications network, a local areanetwork (LAN), a wide area network (WAN), such as the Internet, or acombination of these, and can include wired, wireless, or fiber opticconnections. In general, network 102 can be any combination ofconnections and protocols that will support communications betweencomputing device 104 and other computing devices (not shown) withincomputing environment 100.

Computing device 104 can be any programmable electronic device capableof executing machine-readable instructions, communicating with otherdevices over network 102, and presenting information to a user via auser interface. Computing device 104 includes user interface 106,information recipient 108, filtering component 110, and storedinformation 112. Computing device 104 can include internal and externalhardware components, as depicted and described in further detail withreference to FIG. 4.

User interface 106 provides an interface between a user of computingdevice 104 and computing device 104. User interface 106 can be, but isnot limited to being, a graphical user interface (GUI) or a web userinterface (WUI) and can display text, documents, web browser windows,user options, application interfaces, and instructions for operation,and can include the information (such as graphic, text, and sound)presented to a user and the control sequences the user employs tocontrol information recipient 108 and/or filtering component 110.

Information recipient 108 is a software agent that receives content forconsumption by a user of computing device 104. Information recipient 108can, for example but without limitation, receive information from anindividual content stream (e.g., social media micro-documents such astweets or posts from Twitter or Facebook) or news articles from a newsAPI.

Filtering component 110 performs steps of a personalized contentfiltering method, as described herein with reference to FIG. 2. Forexample, filtering component 110 can interact with information recipient108 to determine which information is displayed on user interface 106and how the information is displayed.

Methods well known in the prior art allow filtering component 110 toidentify that incoming information, also referred to herein as“content,” which can be for example but without limitation a newsarticle, a tweet, a video, or a podcast, is part of an identified storyarc. A story arc is a topic that the user wishes to follow (e.g., atopic about which the user wishes to receive up-to-date information),such as but not limited to a breaking news story or a hashtag (e.g.,“Election 2016,” “Taylor Swift,” “#QOTD”). A user can also designate, byinteraction with user interface 106, a topic of interest that the userwishes to follow.

Filtering component 110 filters content received by informationrecipient 108 based on a user's personal knowledge history, as containedin stored information 112. Stored information 112 can be locatedentirely or in part on computing device 104, or stored information 112can be located remotely on other devices (not shown), such as but notlimited to a server, within computing environment 100. Storedinformation 112 comprises a user-specific knowledge store 114 andnovelty score information, as described herein.

Knowledge store 114 includes one or more user-specific knowledgerepresentations, also referred to herein as “knowledge graphs” or“personal knowledge graphs,” which are based on the user's personalcontent consumption history. Natural language processing methodsemployed by, for example, IBM's Relationship Extraction Service (SIRE)can be used to generate a personal knowledge graph for a specific userand a specific story arc. Filtering component 110 extracts pieces ofinformation such as but not limited to entities, relationships, andfacts from content accessed by the user and populates the user'spersonal knowledge graph with one or more of the pieces of information.Entities are, for example but without limitation, institutions andpublic figures. Relationships are, for example but without limitation,connections between entities, such as but not limited to “born in” and“works for.” Facts are, for example but without limitation,relationships between two or more entities. Illustrative examples ofentities, relationships, and facts are described herein with referenceto FIG. 3A-C.

Filtering component 110 can determine, based on the user having accessedcontent, that the user has consumed (learned) all of the information inthat content, or filtering component 110 can employ additionalmechanisms to determine which information the user has likely learned,and therefore which information can be added to the personal knowledgegraph. For example but without limitation, filtering component 110 candetermine that only information shown in the viewport on user interface106 can be added to the personal knowledge graph; if computing device104 is equipped to track eye movements, filtering component 110 candetermine which information can be added to the personal knowledge graphbased on eye tracking data; or filtering component 110 can determinethat only information that the user selects by a manual choicemechanism, such as highlighting by interaction with user interface 106,can be added to the personal knowledge graph. Other exemplary butnon-limiting factors can include the time that the user spent readingcontent, or the user's posting of a link to the content elsewhereonline.

Filtering component 110 can also, for example, determine a confidencelevel with respect to a particular piece of information, wherein theconfidence level reflects the fidelity of the mechanism (e.g., eyetracking, highlighting) employed to determine that the user has learnedthe piece of information.

The first time that a user accesses content relating to a particularstory arc, filtering component 110 builds an initial personal knowledgegraph based on the information extracted from the content. As the useraccesses (consumes) additional content relating to the same story arc,which can include but is not limited to including clicking on a headlineto read an article, scrolling through headlines or summaries, listeningto a podcast, and clicking on a link to a video received in an emailmessage, filtering component 110 updates the personal knowledge graph.

Filtering component 110 determines the novelty, which can be representedby a novelty score, of incoming content that the user has not accessedbased on the personal knowledge graph. For example, filtering component110 can determine that if the content contains a new entity; a newrelationship; or a new fact (e.g., a relationship that is notrepresented in the personal knowledge graph between a pair of entitiesthat is represented in the personal knowledge graph), such as anadditional fact or a contradictory fact, the novelty score for thatcontent increases. If, for example, filtering component 110 determinesthat the incoming content contains no new information, filteringcomponent 110 can assign a novelty score of ‘0’ to the content.

Filtering component 110 can also calculate cumulative novelty scores forcontent sources, including but not limited to news outlets and personalcontacts on social media, over time. For example, a cumulative noveltyscore for a content source can be based on a ratio of new factsextracted from articles from that content source over new factsextracted from all content sources by filtering component 110 over adefined period of time. Cumulative novelty scores can also be based on,for example but without limitation, veracity based on the number ofretweets or upvotes; the browsing habits of the user; user feedbackregarding novelty, such as clicking a ‘seen it’ or ‘learned somethingnew’ button; the nature of the source, such as a breaking news sourcethat provides up-to-the-minute information, a source that providesevergreen content such as cat videos, or an aggregator source that onlyrepeats information available elsewhere; and user input indicating apreference for a particular author. Filtering component 110 can usecumulative novelty scores to generate, for example, ratings and/orrankings of authors and/or other content sources.

Filtering component 110 uses the individual and relative novelty scoresof incoming content and/or the cumulative novelty scores of contentsources to display, suppress, and/or prioritize (or, generally,“filter”) incoming content. For example but without limitation,filtering component 110 can determine that incoming content with a highnovelty score can be displayed to the user via user interface 106;filtering component 110 can organize incoming content in a news feedbased on the cumulative novelty scores of the content sources; andfiltering component 110 can suppress incoming content with a noveltyscore of ‘0’. Suppressing content can include, for example but withoutlimitation, hiding the content from view on user interface 106, orrelegating the content to the end of a list of headlines displayed onuser interface 106.

FIG. 2 is a flowchart 200 depicting operational steps of a personalizedcontent filtering method, in accordance with an embodiment of thepresent invention.

In step 202, filtering component 110 monitors a user accessing content,extracts information from the content, and determines one or more storyarcs based on the content. Filtering component 110 extracts, forexample, entities, relationships, and facts from the content. Based on,for example, user input to computing device 104 indicating a story arc(e.g., “cute baby animals”) or filtering component 110 identifyingoverlapping information in the content and the existing personalknowledge graph for an identified story arc, filtering component 110determines a story arc that the user wishes to follow.

In step 204, filtering component 110 builds or modifies one or morepersonal knowledge graphs for the one or more story arcs based on theextracted information. For example, filtering component 110 builds a newpersonal knowledge graph, to be stored in knowledge store 114, for anewly identified story arc, or filtering component 110 adds relevant,new information extracted from the content to a personal knowledge graphthat already exists in knowledge store 114 for a previously identifiedstory arc. New information can be, for example, an entity, relationship,or fact extracted from the content that is not already represented inthe personal knowledge graph.

In step 206, filtering component 110 receives new content and determinesthat the user has not accessed the content. New content can be, forexample, a headline or a tweet that has not yet appeared on userinterface 106.

In step 208, filtering component 110 determines a novelty score for thenew content based on prospective changes, due to addition of informationextracted from the new content, to the user's personal knowledgegraph(s). For example, based on a comparison of one or more personalknowledge graphs stored in knowledge store 114 to one or moreprospective knowledge graphs based on the addition of entities,relationships, and facts extracted from the new content, filteringcomponent 110 determines a novelty score for the new content.

In step 210, filtering component 110 displays or suppresses the newcontent based on the novelty score determined in step 208 and/or acumulative novelty score. For example, based on a low novelty score,filtering component 110 can relegate the new content to the end of alist of headlines displayed on user interface 106, or based on a highcumulative novelty score, filtering component 110 can prioritize the newcontent, organizing the list in such a way that the new content appearstoward the top of the list.

In step 212, filtering component 110 updates one or more cumulativenovelty scores based on the novelty score determined in step 208. Forexample, filtering component 110 can update the cumulative novelty scoreof an author of the content and the cumulative novelty score of a newsoutlet that provided that content.

FIG. 3A-C shows examples of knowledge graphs that can be stored inknowledge store 114 and accessed and modified by filtering component110, in accordance with an exemplary embodiment of the presentinvention. The example in FIG. 3A-C follows story arcs related to afictitious event in Toronto, Ontario, used here for the purpose ofillustration, from the perspective of a hypothetical user, named John,of computing device 104.

FIG. 3A shows John's personal knowledge graph, referred to herein aspreliminary knowledge graph 300 (FIG. 3A), after accessing a firstarticle concerning the arrest of Jane Jones, a member of theinternationally famous Prankster Group, for vandalism at the CN Tower inToronto. Preliminary knowledge graph 300 shows the entities andrelationships related to Jane Jones and the Prankster Group andextracted by filtering component 110 from the first article. The firstarticle (not shown) states that “Jane Jones, a Hamilton-based member ofthe Prankster Group, was arrested this week for vandalism at the CNTower, having caused damage to property in the process of putting up anart installation intended as an act of protest.” Nodes 302-316 ofpreliminary knowledge graph 300 represent entities (e.g., “Jane Jones,”“CN Tower”) and connections 318-330 represent relationships betweennodes 302-316 (e.g., “Agent Of,” “Resides In”) related to Jane Jones.Nodes 332-334 represent facts and entities related to the PranksterGroup, and connection 336 represents a relationship between node 332 andnode 334.

FIG. 3B shows prospective knowledge graph 340 constructed by filteringcomponent 110 upon arrival of a second article (not shown), forcomparison with preliminary knowledge graph 300 (not shown) in order todetermine a novelty score for the second article. The second articlecontains information that was not included in the first article, such asprevious acts that the Prankster Group committed in Vancouver. Nodes342-350 represent the new entities extracted from the second article andconnections 352-360 represent relationships between nodes 342-350.Prospective knowledge graph 340 does not replace preliminary knowledgegraph 300 as John's personal knowledge graph unless John accesses thesecond article.

FIG. 3C shows prospective knowledge graph 370 constructed by filteringcomponent 110 upon arrival of a third article (not shown). In thisexample, John has accessed the second article, and prospective knowledgegraph 340 has replaced preliminary knowledge graph 300 as John'spersonal knowledge graph. Connection 372 represents a relationship thatwas not included in the first and second articles. Filtering component110 uses prospective knowledge graph 370 for the purpose of determiningthe novelty of the third article. Prospective knowledge graph 370 doesnot become John's personal knowledge graph unless John accesses thethird article.

FIG. 4 depicts a block diagram 400 of components of computing device 104in computing environment 100, in accordance with illustrativeembodiments of the present invention. It should be appreciated that FIG.4 provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironment may be made.

Computing device 104 includes communications fabric 402, which providescommunications between computer processor(s) 404, memory 406, persistentstorage 408, communications unit 410, and input/output (I/O)interface(s) 412, and cache 414. Communications fabric 402 can beimplemented with any architecture designed for passing data and/orcontrol information between processors (such as microprocessors,communications and network processors, etc.), system memory, peripheraldevices, and any other hardware components within a system. For example,communications fabric 402 can be implemented with one or more buses.

Memory 406 and persistent storage 408 are computer readable storagemedia. In this embodiment, memory 406 includes random access memory(RAM) and cache memory 414. In general, memory 406 can include anysuitable volatile or non-volatile computer readable storage media. Cache414 is a fast memory that enhances the performance of computerprocessor(s) 404 by holding recently accessed data, and data nearaccessed data, from memory 406.

Program instructions and data used to practice embodiments of theinvention, referred to collectively as component(s) 416, are stored inpersistent storage 408 for execution and/or access by one or more of therespective computer processors 404 via one or more memories of memory406. In this embodiment, persistent storage 408 includes a magnetic harddisk drive. Alternatively, or in addition to a magnetic hard disk drive,persistent storage 408 can include a solid state hard drive, asemiconductor storage device, read-only memory (ROM), erasableprogrammable read-only memory (EPROM), flash memory, or any othercomputer readable storage media that is capable of storing programinstructions or digital information.

The media used by persistent storage 408 may also be removable. Forexample, a removable hard drive can be used for persistent storage 408.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer readable storage medium that is also part of persistent storage408.

Communications unit 410, in these examples, provides for communicationswith other data processing systems or devices. Communications unit 410can include one or more network interface cards. Communications unit 410can provide communications through the use of either or both physicaland wireless communications links. Component(s) 416 can be downloaded topersistent storage 408 through communications unit 410.

I/O interface(s) 412 allows for input and output of data with otherdevices that may be connected to computing device 104. For example, I/Ointerface 412 can provide a connection to external devices 418 such as akeyboard, keypad, a touch screen, and/or some other suitable inputdevice. External devices 418 can also include portable computer readablestorage media such as, for example, thumb drives, portable optical ormagnetic disks, and memory cards. Software and data used to practiceembodiments of the present invention, e.g., component(s) 416, can bestored on such portable computer readable storage media and can beloaded onto persistent storage 408 via I/O interface(s) 412. I/Ointerface(s) 412 also connect to a display 420.

Display 420 provides a mechanism to display data to a user and may be,for example, a touchscreen.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The terminology used herein was chosen to best explain the principles ofthe embodiment, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A computer-implemented method for personalizedcontent filtering, the method comprising: determining, by the one ormore computer processors, a first story arc based on a first piece ofcontent; creating, by the one or more computer processors, a personalknowledge graph, of only the user, representing the user's individualknowledge related to the first story arc; determining, by the one ormore computer processors, responsive to receiving a second piece ofcontent, wherein the second piece of content has not been accessed bythe user, a second story arc based on the second piece of content;determining, by the one or more computer processors, that the user hasaccessed the second piece of content; updating, by the one or morecomputer processors, the personal knowledge graph based on the secondpiece of content, wherein the first piece of content and the secondpiece of content share a third story arc, or determining a secondpersonal knowledge graph based on the second story arc; determining, bythe one or more computer processors, which information in the secondpiece of content the user has consumed; determining, by the one or morecomputer processors, a novelty score for the second piece of contentbased on the personal knowledge graph; and filtering, by the one or morecomputer processors, the second piece of content based on the noveltyscore and the user posting a link to the content elsewhere online. 2.The computer-implemented method of claim 1, wherein the personalknowledge graph comprises entities, relationships, and facts extractedfrom accessed content.
 3. The computer-implemented method of claim 1,wherein the novelty score is based on the existence of one or moreentities, relationships, and facts extracted from the second piece ofcontent and not represented in the personal knowledge graph.
 4. Thecomputer-implemented method of claim 1, wherein filtering comprises oneof hiding the second piece of content, prioritizing the second piece ofcontent, and deprioritizing the second piece of content.
 5. Thecomputer-implemented method of claim 1, further comprising: determiningor updating, by the one or more computer processors, a cumulativenovelty score for a content source based on the determined noveltyscore.
 6. The computer-implemented method of claim 1, wherein thefiltering is further based on the user posting links associated with theportions of the second piece of content to other locations online. 7.The computer-implemented method of claim 1, wherein the filtering isfurther based on at least one of portions of the second piece of contentdisplayed to the user, eye tracking data associated with the userviewing the portions of the second piece of content, user manualselection of the portions of the second piece of content or time theuser spent reading the portions of the second piece of content exceedinga predefined threshold.
 8. A computer program product for personalizedcontent filtering, the computer program product comprising: one or morenon-transitory computer readable storage media and program instructionsstored on the one or more non-transitory computer readable storagemedia, the program instructions comprising: program instructions todetermine a first story arc based on a first piece of content; programinstructions to create a personal knowledge graph, of only the user,representing the user's individual knowledge related to the first storyarc; program instructions to determine, responsive to receiving a secondpiece of content, wherein the second piece of content has not beenaccessed by the user, a second story arc based on the second piece ofcontent; program instructions to determine that the user has accessedthe second piece of content; program instructions to update the personalknowledge graph based on the second piece of content, wherein the firstpiece of content and the second piece of content share a third storyarc, or determining a second personal knowledge graph based on thesecond story arc; program instructions to determine which information inthe second piece of content the user has consumed; program instructionsto determine a novelty score for the second piece of content based onthe personal knowledge graph; program instructions to filter the secondpiece of content based on the personal knowledge graph; and programinstructions to filter the second piece of content based on the noveltyscore and the user posting a link to the content elsewhere online. 9.The computer program product of claim 8, wherein the personal knowledgegraph comprises entities, relationships, and facts extracted fromaccessed content.
 10. The computer program product of claim 8, whereinthe novelty score is based on the existence of one or more entities,relationships, and facts extracted from the second piece of content andnot represented in the personal knowledge graph.
 11. The computerprogram product of claim 8, wherein filtering comprises at least one ofhiding the second piece of content, prioritizing the second piece ofcontent or deprioritizing the second piece of content.
 12. The computerprogram product of claim 8, further comprising: program instructions todetermine or update a cumulative novelty score for a content sourcebased on the determined novelty score.
 13. The computer program productof claim 8, wherein the filtering is further based on the user postinglinks associated with the portions of the second piece of content toother locations online.
 14. The computer program product of claim 8,wherein the filtering is further based on at least one of portions ofthe second piece of content displayed to the user, eye tracking dataassociated with the user viewing the portions of the second piece ofcontent, user manual selection of the portions of the second piece ofcontent or time the user spent reading the portions of the second pieceof content exceeding a predefined threshold.
 15. A computer system forpersonalized content filtering, the computer system comprising: one ormore processors; one or more computer readable storage media; andprogram instructions stored on the one or more computer readable storagemedia for execution by at least one of the one or more processors, theprogram instructions comprising: program instructions to determine afirst story arc based on a first piece of content; program instructionsto create a personal knowledge graph, of only the user, representing theuser's individual knowledge related to the first story arc; programinstructions to determine, responsive to receiving a second piece ofcontent, wherein the second piece of content has not been accessed bythe user, a second story arc based on the second piece of content;program instructions to determine that the user has accessed the secondpiece of content; program instructions to update the personal knowledgegraph based on the second piece of content, wherein the first piece ofcontent and the second piece of content share a third story arc, ordetermining a second personal knowledge graph based on the second storyarc; program instructions to determine which information in the secondpiece of content the user has consumed; program instructions todetermine a novelty score for the second piece of content based on thepersonal knowledge graph; program instructions to filter the secondpiece of content based on the personal knowledge graph; and programinstructions to filter the second piece of content based on the noveltyscore and the user posting a link to the content elsewhere online. 16.The computer system of claim 15, wherein the personal knowledge graphcomprises entities, relationships, and facts extracted from accessedcontent.
 17. The computer system of claim 15, wherein the novelty scoreis based on the existence of one or more entities, relationships, andfacts extracted from the second piece of content and not represented inthe personal knowledge graph.
 18. The computer system of claim 15,wherein filtering comprises one of hiding the second piece of content,prioritizing the second piece of content, and deprioritizing the secondpiece of content.
 19. The computer system of claim 15, furthercomprising: program instructions to determine or update a cumulativenovelty score for a content source based on the determined noveltyscore.
 20. The computer system of claim 15, wherein the filtering isfurther based on the user posting links associated with the portions ofthe second piece of content to other locations online and at least oneof portions of the second piece of content displayed to the user, eyetracking data associated with the user viewing the portions of thesecond piece of content, user manual selection of the portions of thesecond piece of content or time the user spent reading the portions ofthe second piece of content exceeding a predefined threshold.